Vulcan/SystemPrototype

Overview

The prototype is designed to work on three questions. We want the system to output the following:

• Score for the input proposition.

• New facts inferred.

• Facts and rules used in scoring.

Status

The MLN programs and output from Tuffy can be found here.

• Knowledge: Worked out the facts and rules required.
• Score: The system outputs scores for each query predicate. If query is not in output then score is zero.
• Does it work?

• In all three examples the correct answer is assigned higher score compared to the incorrect ones.
• Facts inferred by larger number of steps have a lower score compared to those inferred by a smaller number of steps.

• What other diagnostics do we have?

• Inferred facts along with their probabilities.
• Rules that are reachable from the query fact. i.e., Clauses in the MLN that are relevant to the inference of the query fact.

• What diagnostics do we NOT have?

• Connections between the clauses in the MLN.
• A reconstruction/visualization of the MLN network.

• What does this exercise suggest?

• Use predicates with small arity. For example, avoid writing rules entire nested tuples as predicates.
• For now we can compute this from the score of its components: Score(nested_tuple) = Score(top tuple) * Score (nested).